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Record W6925182463 · doi:10.17605/osf.io/jy6cd

Global Investigation of Current Indigenous Readiness to Practice Models and Measurement Tools: A Rapid Review

2022· other· en· W6925182463 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2022
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPreparednessWork (physics)Community of practiceCommunity practiceFoundation (evidence)

Abstract

fetched live from OpenAlex

The readiness to practice of individuals working with Indigenous communities is paramount in understanding the current state of Indigenous health and wellness in Canada. Individuals who choose to work in Indigenous communities must understand and appreciate the complexities of their culture and knowledge of their social and political history. The aim of this review is to better understand readiness to practice and/or preparedness for practice as it pertains to community and clinical work with Canadian Indigenous peoples. This research will serve as the foundation for the development and implementation of an assessment tool to measure longitudinal changes in individual’s readiness and/or preparedness to practice in Indigenous contexts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.068
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.152
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0170.017
Science and technology studies0.0010.004
Scholarly communication0.0060.006
Open science0.0040.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.308
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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